Deciding between man and zone coverage is one of the most critical strategic decisions a defensive coordinator must take before each offensive play in American football. While experienced offensive coordinators and quarterbacks often rely on visual cues to identify these defensive schemes, the increasing availability of player tracking data offers a new avenue to uncover and analyze these tactics. A notable example is Amazon’s NFL Next Gen Stats model, which delivers coverage predictions during live broadcasts (see a snapshot of the 2024 Week 12 match between the Pittsburgh Steelers and Cleveland Browns). However, in these pre-snap motion does not seem to play an accentuated role (see Amazon), although it is a crucial element of modern offensive strategies and it is often used to detect the hidden defensive scheme.
Hence, our contribution explores the potential of the information in pre-snap motion, previously omitted. While we similarly predict man- or zone coverage when the teams are set before snaps, we further leverage the additional information of pre-snap player movements. Specifically, in addition to including rather naive post-motion features, we use a hidden Markov model (HMM) to model defenders’ trajectories based on hidden states, which represent the offensive players they may be guarding. Incorporating summary statistics of the state decoding results as features into the existing models substantially improves the predictive ability. This lays the groundwork for further analyses such as the evaluation of the effectiveness of pre-snap motion in uncovering defensive strategies.
We aim to forecast the defensive scheme (man- or zone defense) using
the pff_passCoverage indicator in play-by play data. We
omit plays tagged as others as well as plays with more than
five offensive linemen and with two quarterbacks. Since we are
specifically interested in analyzing pre-snap player movements, we
concentrate on plays that contain any pre-snap motion. Ultimately, we
end up with \(3985\) offensive plays in
total, from which the defense played \(2973\) in zone and \(1012\) in man coverage.
To accurately forecast the defensive scheme (man- or zone defense) for every play, we create various features derived from the tracking data. In particular, we conducted the following feature engineering steps: First, using all 11 players on each side, we compute the area spanned by the convex hull of a team as well as the largest \(y\) distance (i.e. the width of the hull) and the largest \(x\) distance (i.e. the length of the hull). Then, we select the five most relevant players on each side of the field. For offense, we omit the offensive line and the QB, thus coming up with five players that are a composition of running backs, tight ends and wide receivers. In contrast, for defense, disregarding defense liners (NT, DT, DE), we select the five defenders that were the closest to the five offensive players, corresponding to a weighted euclidean distance, putting much more emphasis on the y-axis. From these 10 players, we derive features related to their (standardized) position, distance and orientation. Additionally, we extract relevant information from the play-by-play data, such as quarter, down, yards to go, home and away score and the remaining game time in the current half (in seconds). See the Appendix for a more detailed description of the features.
We train different models to predict whether the defense plays a man- or zone coverage scheme. Since the aim of the project is to show the effectiveness of pre-snap motion, we follow a three-step approach:
In general, we have a limited dataset available (only 3985 plays) and therefore need to manage model complexity by controlling the number of features. Given the small dataset, we focus on 32 previously described basic features used in all 3 models: 6 convex hull related features, 20 player features, and 6 play-by-play features. In the Appendix, we provide a discussion on the choice of features.
First of all, we need a suitable basic model class for predicting man
or zone coverage and opt for the following two: First, we fit a
glmnet (elastic net) model, which performs implicit feature
selection and is able to handle multicollinearity. Second, we use an
xgboost model, which is able to capture non-linear effects
(and interactions) and also handles multicollinearity. However, it
necessitates careful hyperparameter tuning and generally performs better
on bigger data sets. For all of the models, we use 10-fold cross
validation on a suitable hyperparameter grid. Ich finde der Teil ist besser in der Einleitung zur
Analyse, da wir die zwei Modellklassen ja für alle 3 Modelle verwenden.
Daher auch nochmal die Beschreibung der Features oben, weil die 32
features sind einfach für alle Modell gleich.
We fit the models with the previously described basic features. All of these features are derived at the time of line-set, which is why these models do not use any pre-snap motion information. These very basic models serve as baseline models that allows to measure the effect of pre-snap motion (features) in the following.
In a second step, we extend our basic pre-motion model with naive post-motion features. To keep the complexity manageable, we derive only 6 additional post-motion features: for each team (offense and defense), we infer the maximum \(y\)-distance, the maximum \(x\)-distance and the total distance traveled by both teams until the snap.